{"paper_id":"b25324e0-08e1-4cc7-9d00-edb81149d656","body_text":"Technical Note\nEnhanced proteome profiling of human cerebrospinal fluid using \na commercial plasma enrichment strategy\nEva Borràs1,2, Federica Anastasi1,3,4, Olga Pastor1,2, Marc Suárez-Calvet3,4,5, Eduard Sabidó1,2\n1. Centre for Genomic Regulation (CRG), Dr Aiguader 88, 08003 Barcelona, Spain\n2. Universitat Pompeu Fabra (UPF), Barcelona, Spain\n3. Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain\n4. Hospital del Mar Medical Research Institute, Barcelona, Spain\n5. Servei de Neurologia, Hospital del Mar, Barcelona, Spain.\nAbstract\nCerebrospinal fluid (CSF) is a valuable liquid biopsy for identifying protein biomarkers in neurological diseases,  \nyet its proteome profiling faces challenges due to the large dynamic range of protein abundances. In this study,  \nwe assessed the effectiveness of a commercial enrichment strategy, initially developed for plasma samples, in  \nenhancing the detection of low-abundance proteins in human CSF. We demonstrate significant improvements in  \nprotein identification and coverage depth while maintaining high reproducibility and low coefficients of variation.  \nThese findings underscore the potential of this enrichment strategy to facilitate rapid and sensitive CSF analysis,  \nadvancing biomarker discovery in neurological research.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint \n\nCerebrospinal fluid (CSF) is a valuable liquid biopsy for neurological diseases research to identify protein  \nbiomarkers for diagnostic purposes, 1 predict treatment response, 2 and to understand the molecular mechanisms  \ninvolved in disease progression.3–7 However, profiling the human CSF proteome by mass spectrometry faces two \nmain analytical challenges. Firstly, the broad dynamic range of protein abundances commonly seen in liquid  \nbiopsies, which hinders the sensitivity and the ability to measure low-abundant proteins. Secondly, the need to  \nprepare and analyze hundreds of human samples in a reproducible and timely manner. Indeed, there has been a \ntrade-off among sensitivity, number of detected analytes, number of samples, sample amount, and data quality.  \nMultiplexed immunoassays and aptamer-based approaches have recently been developed to address these  \nanalytical challenges; 8,9 however, they are limited by their targeted nature, as they can only identify a pre-\nselected panel of proteins. Recent advancements in mass spectrometry instrumentation have facilitated the  \nrapid acquisition of liquid biopsies, 10–13 which is essential for analyzing large patient cohorts in translational  \nclinical  projects.  Concurrently,  several  sample  preparation  commercial  solutions  have  been  introduced  to  \naddress the challenges associated with the extensive dynamic range of liquid biopsies, and enhance in-depth  \nprotein identification and increase sample throughput. These new approaches offer efficient alternatives to  \nexisting classical strategies like antibody-based protein depletion and sample fractionation strategies, 14,15 and \ninclude  the  use  of  nanoparticle  protein  coronas, 16,17 hyper-porous  strong-anion  exchange  magnetic  \nmicroparticles,18 and s eeded precipitation on paramagnetic beads to enrich low abundant proteins .19 Despite  \ntheir demonstrated superior performance in plasma samples,20 their potential to enhance the analytical sensitivity \nof CSF remains to be established. In this study, we assessed the PreOmics ENRICH-iST kit protocol—originally  \ndesigned to reduce th e dynamic range in plasma samples—for processing CSF, and compared its performance  \nto the analysis of neat CSF samples. We assessed various amounts of CSF starting material, and evaluated the  \nsensitivity and reproducibility of this enrichment strategy.\nInitially, a pool of human CSF samples was obtained from two individuals with Alzheimer’s disease dementia and \nmild cognitive impairment, and it was consistently used in all subsequent experiments. The pooled CSF sample  \nwas processed in varying amounts, employing a tryptic digestion directly on the neat CSF samples, or after an  \nenrichment procedure using paramagnetic beads. For the analysis of neat CSF, the PreOmics iST-BCT 8x kit  \nwas used, processing 10 µL of CSF according to the manufacturer's protocol to obtain the peptide mix prior to  \nanalysis by liquid chromatography coupled to mass spectrometry (LC-MS). For the enrichment of low-abundant  \nproteins, we employed the ENRICH iST-BCT 8x kit, which had been previously optimized for 20 µL of plasma. 19 \nImportantly, CSF samples have a protein concentration approximately 100 times lower than that of neat plasma  \n(plasma: ~50 µg/µL; CSF: ~0,25-0.5 µg/µL), thus requiring the evaluation of higher volumes of CSF as part of  \nour optimization process. We processed three different starting amounts of CSF representing a volume increase  \nranging from 2.5- to 25-fold compared to the one recommended for plasma (Figure 1A). The binding buffer used \nduring the enrichment process had also to be adapted. We therefore assessed several amounts of binding buffer \nin conjunction with different volumes of CSF. The specific conditions tested included 50 µL of CSF in 70 µL of  \nbinding buffer, 150 µL of CSF in 200 µL of binding buffer, and 500 µL of CSF in 700 µL of binding buffer. The  \nvolumes tested were carefully chosen to maximize protein processing while minimizing sample usage due to the  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint \n\nintrinsic limited availability of human CSF samples, and the need to ensure that the selected volumes fit within  \nthe volume constraints of the kit. In all cases, 10% of the digested enriched eluate was loaded into the analytical  \ncolumn. For the neat CSF, ca. 1 µg was loaded into the column in the Orbitrap Eclipse system, and ca. 100 ng \nwere loaded into the column in the Orbitrap Astral, following the recommended amounts for each platform. All  \nexperiments were performed in technical triplicate starting from the same pool of human CSF sample.\nDigested  and  enriched  samples  were  analyzed  by  LC-MS  using  data-independent  acquisition  across  two  \ndifferent instruments: an Orbitrap Eclipse Tribrid, and an Orbitrap Astral. Chromatography settings and data-\nacquisition schema were tailored for each platform ( Supporting Information). Acquired spectra were analyzed  \nusing a library-free strategy with D IA-NN (Neural networks and interference correction enable deep proteome  \ncoverage in high throughput) (v1.8.1). The data were searched against a Swiss-Prot human database (as in April \n2023) plus a list of common contaminants, and all the corresponding decoy entries. For peptide identification  \ntrypsin was chosen as enzyme and up to one miscleavage was allowed. Oxidation of methionine was used as  \nvariable  modification  whereas  carbamid omethylation  on  cysteines  was  set  as  a  fixed  modification.  False  \ndiscovery rate (FDR) was set to a maximum of 1% at peptide and protein level. Precurs or and fragment ion m/z  \nmass range were adjusted to 500-900 and 350-1850, respectively. For peptide quantification match-between-\nruns was enabled, protein inference was set to ‘Protein names (from FASTA)’ with ’Heuristic protein inference’  \noption and the quantification strategy was set to ‘Robust LC (high precision)’. Default settings were used for the  \nother parameters. The mass spectrometry proteomics data have been deposited to the ProteomeXchange  \nConsortium via the PRIDE partner repository with the dataset identifier PXD055853.21\nResults were initially analyzed in terms of protein identifications to determine whether the enrichment procedure  \nled  to  the  identification  of  more  analytes  compared  to  the  neat  CSF  analysis  ( Figure  1B  and  1C, \nSupplementary Table S1 ). In the results generated from the Orbitrap Eclipse, we observed an increase in  \nprotein identification in the enriched samples when using at least 150 µL and 500 µL of starting CSF material.  \nSimilarly, in the Orbitrap Astral, there was a significant increase in the number of protein identifications when  \nenriching CSF samples no matter the starting volume (50 µL, 150 µL, 500 µL), nor the data acquisition strategy  \nused (30 SPD, 60 SPD). In all cases, the number of identified protein groups in enriched samples increased with  \nthe initial volume of CSF increased, indicating that the enrichment procedure had not reached saturation.  \nHowever, larger CSF volumes were not evaluated due to limitations in sample availability, constraints of the kit,  \nand, importantly, because using larger volumes would not provide clinically translatable results. In the case of  \nthe Orbitrap Eclipse, no significant increase in the number of identified proteins was observed when enriching  \nlow amounts of CSF (i.e., 50 µL) compared to the neat CSF. In contrast, a clear difference was noted in the  \nOrbitrap Astral between neat CSF and low amounts of CSF (i.e., 50 µL). This observation is likely due to the  \nexcellent analytical performance of the Orbitrap Eclipse system when operated with high amounts of neat CSF in \ncombination with the use of a 50-cm column and a 2-hour gradient. Conversely, in the Orbitrap Astral, the lower  \nloading amounts and shorter gradients used ( Supporting Information ) probably limited the comprehensive  \nanalysis  of  neat  CSF.  In  these  high-throughput  analyses,  the  use  of  the  enrichment  process  results  in  a \nconsiderable gain in the number of identified proteins, even when starting th e enrichment procedure with only 50 \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint \n\nµL of CSF. In the most favorable conditions tested, up to 2,616 protein groups and 23,875 pept ide precursors \nwere identified when analyzing all 3 replicates together (30 SPD, Orbitrap Astral, 500 µL of CSF). Nevertheless,  \nutilizing 150 µl of CSF in conjunction with short data acquisition gradients (e.g., 30 SPD) probably represents the  \napproach with the highest translational potential, as it effectively balances analysis time, sample volume, and the  \nnumber of identified peptide precursors and protein  groups. Interestin gly, when focusing on specific proteins  \npreviously linked to diseases such as multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer’s, Parkinson’s  \ndisease, and other neurodegenerative diseases, we observed a preferential identification of these proteins in  \nsamples that had been enriched with paramagnetic beads ( Table 1). It is important to note that some of the  \nproteins of interest are typically present only in the late stages of certain diseases (e.g. Frataxin), which explains  \ntheir absence in the CSF samples from the donors used in this study, regardless of the strategy employed.\nBeyond the identification of proteins and peptides, it is essential that the enrichment procedure is reproducible to  \nensure consistent analysis of the CSF. To this end, we assessed the reproducibility of the CSF enrichment  \nprocedure  by  analyzing  technical  replicates  and  calculating  the  coefficients  of  variation  for  precursor  \nabundances. We found a strong linear correlation among replicates, with an r value between 0.88-0.92 for the  \nenriched samples analyzed on the Orbitrap Eclipse, comparable to the correlation observed with  neat CSF  \nsamples with r values of  ca. 0.94 ( Figure 1D ). Additionally, the coefficients of variation were predominantly  \nbelow 15% across all conditions tested, and although a slight increase in variation was noted with higher  \nvolumes of enriched CSF, the results remained consistent with those obtained from neat CSF samples ( Figure \n1E).\nIn conclusion, our study successfully evaluat ed the use of the PreOmics ENRICH-iST kit, an enrichment sample  \npreparation strategy, originally designed for plasma, for processing CSF samples. By adapting this commercially  \navailable enrichment strategy, we significantly enhanced proteome coverage and depth in human CSF while  \nensuring high technical reproducibility and low coefficients of variation. A known limitation of this study is the  \ninability to conduct a comprehensive assessment of additional analytical conditions that would be of interest from \na strictly analytical perspective. This constraint arises from the limited availability of samples and the ethical  \nconsiderations associated with the use of human CSF. However, our findings underscore the potential of such  \nenrichment methods to improve the identification of low-abundant proteins in human CSF, which is crucial for  \nadvancing biomarker discovery and clinical applications in neurological disease research. The integration of  \noptimized sample preparation techniques with cutting-edge mass spectrometry instrumentation will certainly  \nfacilitate  rapid  and  sensitive  analysis  of  CSF  samples,  thereby  supporting  large-scale  studies  aimed  at  \nunderstanding complex neurological conditions.\nAcknowledgements\nThe authors would like to express their most sincere gratitude to the staff of the Neurology department at  \nHospital del Mar and the BIODEGMAR participants and relatives without whom this research would have not  \nbeen possible. We acknowledge support of the Spanish Ministry of Science and Innovation through the Centro  \nde Excelencia Severo Ochoa (CEX2020-001049-S grant funded by MCIN/AEI/10.13039/501100011033) and  \n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint \n\nPID2020-115092GB-I00 funded by AEI/10.13039/501100011033, and the Generalitat de Catalunya through the  \nCERCA programme and the Departament de Recerca i Universitats (2021-SGR2021-01225). This result is part  \nof  a  project  that  has  received  funding  from  the  European  Union's  Horizon  2020  research  and  innovation \nprogramme under the Marie Sklodowska-Curie grant agreement No 956148. The CRG/UPF Proteomics Unit is  \npart of the Spanish Infrastructure for Omics Technologies (ICTS OmicsTech). FA receives funding from the  \nJDC2022-049347-I  grant,  funded  by  the  MCIU/AEI/10.13039/501100011033  and  the  European  Union  \nNextGenerationEU/PRTR.  MSC  receives  funding  from  the  European  Research  Council  (ERC)  under  the  \nEuropean Union’s Horizon 2020 research and innovation program (Grant agreement No. 948677), the Instituto  \nde  Salud  Carlos  III  through  the  projects  PI19/00155  and  PI22/00456  (Co-funded  by  European  Regional  \nDevelopment Fund (FEDER) \"A way to make Europe\"), and receives the support of a fellowship from ”la Caixa”  \nFoundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme  \nunder the Marie Skłodowska-Curie grant agreement No 847648 (fellowship code LCF/BQ/PR21/11840004). \nEthical Statement\nThe BIODEGMAR study was approved by the Independent Ethics Committee “Parc de Salut Mar”, Barcelona  \n(CEIC PSMAR, project code 2018/7805I). All participants from BIODEGMAR provided informed consent.\nNotes\nThe authors declare no competing financial interest. Note that the ENRICH-iST kits used in this work were  \nprovided free-of-charge by PreOmics Gmbh.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint \n\nBibliography\n(1) Cantó, E.; Tintoré, M.; Villar, L. M.; Borrás, E.; Alvarez-Cermeño, J. C.; Chiva, C.; Sabidó, E.; Rovira, A.; Montalban, X.; Comabella,  \nM. Validation of Semaphorin 7A and Ala-β-His-Dipeptidase as Biomarkers Associated with the Conversion from Clinically Isolated  \nSyndrome to Multiple Sclerosis. J Neuroinflammation 2014, 11, 181. https://doi.org/10.1186/s12974-014-0181-8.\n(2) Faravelli, I.; Gagliardi, D.; Abati, E.; Meneri, M.; Ongaro, J.; Magri, F.; Parente, V.; Petrozzi, L.; Ricci, G.; Farè, F.; Garrone, G.;  \nFontana, M.; Caruso, D.; Siciliano, G.; Comi, G. P.; Govoni, A.; Corti, S.; Ottoboni, L. Multi-Omics Profiling of CSF from Spinal  \nMuscular Atrophy Type 3 Patients after Nusinersen Treatment: A 2-Year Follow-up Multicenter Retrospective Study. Cell Mol Life Sci  \n2023, 80 (8), 241. https://doi.org/10.1007/s00018-023-04885-7.\n(3) Fissolo, N.; Matute-Blanch, C.; Osman, M.; Costa, C.; Pinteac, R.; Miró, B.; Sanchez, A.; Brito, V.; Dujmovic, I.; Voortman, M.; Khalil,  \nM.; Borràs, E.; Sabidó, E.; Issazadeh-Navikas, S.; Montalban, X.; Comabella Lopez, M. CSF SERPINA3 Levels Are Elevated in  \nPatients  With  Progressive  MS.  Neurol  Neuroimmunol  Neuroinflamm  2021,  8 (2),  e941.  \nhttps://doi.org/10.1212/NXI.0000000000000941.\n(4) Comabella, M.; Sastre-Garriga, J.; Borras, E.; Villar, L. M.; Saiz, A.; Martínez-Yélamos, S.; García-Merino, J. A.; Pinteac, R.; Fissolo,  \nN.; Sánchez López, A. J.; Costa-Frossard, L.; Blanco, Y.; Llufriu, S.; Vidal-Jordana, A.; Sabidó, E.; Montalban, X. CSF Chitinase 3-\nLike 2 Is Associated With Long-Term Disability Progression in Patients With Progressive Multiple Sclerosis. Neurol Neuroimmunol \nNeuroinflamm 2021, 8 (6), e1082. https://doi.org/10.1212/NXI.0000000000001082.\n(5) Lleó, A.; Núñez-Llaves, R.; Alcolea, D.; Chiva, C.; Balateu-Paños, D.; Colom-Cadena, M.; Gomez-Giro, G.; Muñoz, L.; Querol-\nVilaseca, M.; Pegueroles, J.; Rami, L.; Lladó, A.; Molinuevo, J. L.; Tainta, M.; Clarimón, J.; Spires-Jones, T.; Blesa, R.; Fortea, J.;  \nMartínez-Lage, P.; Sánchez-Valle, R.; Sabidó, E.; Bayés, À.; Belbin, O. Changes in Synaptic Proteins Precede Neurodegeneration  \nMarkers  in  Preclinical  Alzheimer’s  Disease  Cerebrospinal  Fluid.  Mol  Cell  Proteomics  2019,  18 (3),  546–560.  \nhttps://doi.org/10.1074/mcp.RA118.001290.\n(6) Tristán-Noguero, A.; Borràs, E.; Molero-Luis, M.; Wassenberg, T.; Peters, T.; Verbeek, M. M.; Willemsen, M.; Opladen, T.; Jeltsch, K.;  \nPons, R.; Thony, B.; Horvath, G.; Yapici, Z.; Friedman, J.; Hyland, K.; Agosta, G. E.; López-Laso, E.; Artuch, R.; Sabidó, E.; García-\nCazorla,  À.  Novel  Protein  Biomarkers  of  Monoamine  Metabolism  Defects  Correlate  with  Disease  Severity.  Mov  Disord 2020. \nhttps://doi.org/10.1002/mds.28362.\n(7) Tijms, B. M.; Vromen, E. M.; Mjaavatten, O.; Holstege, H.; Reus, L. M.; van der Lee, S.; Wesenhagen, K. E. J.; Lorenzini, L.; Vermunt, \nL.; Venkatraghavan, V.; Tesi, N.; Tomassen, J.; den Braber, A.; Goossens, J.; Vanmechelen, E.; Barkhof, F.; Pijnenburg, Y. A. L.; van  \nder Flier, W. M.; Teunissen, C. E.; Berven, F. S.; Visser, P. J. Cerebrospinal Fluid Proteomics in Patients with Alzheimer’s Disease  \nReveals Five Molecular Subtypes with Distinct Genetic Risk Profiles. Nat Aging 2024, 4 (1), 33–47. https://doi.org/10.1038/s43587-\n023-00550-7.\n(8) Ellington, A. D.; Szostak, J. W. In Vitro Selection of RNA Molecules That Bind Specific Ligands. Nature 1990, 346 (6287), 818–822.  \nhttps://doi.org/10.1038/346818a0.\n(9) Fredriksson, S.; Gullberg, M.; Jarvius, J.; Olsson, C.; Pietras, K.; Gústafsdóttir, S. M.; Ostman, A.; Landegren, U. Protein Detection  \nUsing Proximity-Dependent DNA Ligation Assays. Nat Biotechnol 2002, 20 (5), 473–477. https://doi.org/10.1038/nbt0502-473.\n(10) Stewart, H. I.; Grinfeld, D.; Giannakopulos, A.; Petzoldt, J.; Shanley, T.; Garland, M.; Denisov, E.; Peterson, A. C.; Damoc, E.; Zeller,  \nM.; Arrey, T. N.; Pashkova, A.; Renuse, S.; Hakimi, A.; Kühn, A.; Biel, M.; Kreutzmann, A.; Hagedorn, B.; Colonius, I.; Schütz, A.;  \nStefes, A.; Dwivedi, A.; Mourad, D.; Hoek, M.; Reitemeier, B.; Cochems, P.; Kholomeev, A.; Ostermann, R.; Quiring, G.; Ochmann, M.; \nMöhring, S.; Wagner, A.; Petker, A.; Kanngiesser, S.; Wiedemeyer, M.; Balschun, W.; Hermanson, D.; Zabrouskov, V.; Makarov, A. A.; \nHock, C. Parallelized Acquisition of Orbitrap and Astral Analyzers Enables High-Throughput Quantitative Analysis. Anal Chem 2023, \n95 (42), 15656–15664. https://doi.org/10.1021/acs.analchem.3c02856.\n(11) Heil, L. R.; Damoc, E.; Arrey, T. N.; Pashkova, A.; Denisov, E.; Petzoldt, J.; Peterson, A. C.; Hsu, C.; Searle, B. C.; Shulman, N.; Riffle, \nM.; Connolly, B.; MacLean, B. X.; Remes, P. M.; Senko, M. W.; Stewart, H. I.; Hock, C.; Makarov, A. A.; Hermanson, D.; Zabrouskov,  \nV.; Wu, C. C.; MacCoss, M. J. Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-\nIndependent Acquisition. J Proteome Res 2023, 22 (10), 3290–3300. https://doi.org/10.1021/acs.jproteome.3c00357.\n(12) Mun, D.-G.; Budhraja, R.; Bhat, F. A.; Zenka, R. M.; Johnson, K. L.; Moghekar, A.; Pandey, A. Four-Dimensional Proteomics Analysis  \nof  Human  Cerebrospinal  Fluid  with  Trapped  Ion  Mobility  Spectrometry  Using  PASEF.  Proteomics 2023,  23\n (10),  e2200507.  \nhttps://doi.org/10.1002/pmic.202200507.\n(13) Vitko, D.; Chou, W.-F.; Nouri Golmaei, S.; Lee, J.-Y.; Belthangady, C.; Blume, J.; Chan, J. K.; Flores-Campuzano, G.; Hu, Y.; Liu, M.;  \nMarispini, M. A.; Mora, M. G.; Ramaswamy, S.; Ranjan, P.; Williams, P. B.; Zawada, R. J. X.; Ma, P.; Wilcox, B. E. timsTOF HT  \nImproves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery. J Proteome \nRes 2024, 23 (3), 929–938. https://doi.org/10.1021/acs.jproteome.3c00646.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint \n\n(14) Lee, P. Y.; Osman, J.; Low, T. Y.; Jamal, R. Plasma/Serum Proteomics: Depletion Strategies for Reducing High-Abundance Proteins  \nfor Biomarker Discovery. Bioanalysis 2019, 11 (19), 1799–1812. https://doi.org/10.4155/bio-2019-0145.\n(15) Tam, S. W.; Pirro, J.; Hinerfeld, D. Depletion and Fractionation Technologies in Plasma Proteomic Analysis. Expert Rev Proteomics  \n2004, 1 (4), 411–420. https://doi.org/10.1586/14789450.1.4.411.\n(16) Blume, J. E.; Manning, W. C.; Troiano, G.; Hornburg, D.; Figa, M.; Hesterberg, L.; Platt, T. L.; Zhao, X.; Cuaresma, R. A.; Everley, P.  \nA.; Ko, M.; Liou, H.; Mahoney, M.; Ferdosi, S.; Elgierari, E. M.; Stolarczyk, C.; Tangeysh, B.; Xia, H.; Benz, R.; Siddiqui, A.; Carr, S. A.; \nMa, P.; Langer, R.; Farias, V.; Farokhzad, O. C. Rapid, Deep and Precise Profiling of the Plasma Proteome with Multi-Nanoparticle  \nProtein Corona. Nat Commun 2020, 11 (1), 3662. https://doi.org/10.1038/s41467-020-17033-7.\n(17) Ashkarran, A. A.; Gharibi, H.; Voke, E.; Landry, M. P.; Saei, A. A.; Mahmoudi, M. Measurements of Heterogeneity in Proteomics  \nAnalysis of the Nanoparticle Protein Corona across Core Facilities. Nat Commun 2022, 13 (1), 6610. https://doi.org/10.1038/s41467-\n022-34438-8.\n(18) Wu, C. C.; Tsantilas, K. A.; Park, J.; Plubell, D.; Sanders, J. A.; Naicker, P.; Govender, I.; Buthelezi, S.; Stoychev, S.; Jordaan, J.;  \nMerrihew, G.; Huang, E.; Parker, E. D.; Riffle, M.; Hoofnagle, A. N.; Noble, W. S.; Poston, K. L.; Montine, T. J.; MacCoss, M. J. Mag-\nNet: Rapid Enrichment of Membrane-Bound Particles Enables High Coverage Quantitative Analysis of the Plasma Proteome. bioRxiv \n2024, 2023.06.10.544439. https://doi.org/10.1101/2023.06.10.544439.\n(19) Resources | Preomics. https://www.preomics.com/resources (accessed 2024-08-30).\n(20) Soni, R. K. Frontiers in Plasma Proteome Profiling Platforms: Innovations and Applications.  Clin Proteomics  2024,  21 (1), 43.  \nhttps://doi.org/10.1186/s12014-024-09497-2.\n(21) Perez-Riverol, Y.; Bai, J.; Bandla, C.; García-Seisdedos, D.; Hewapathirana, S.; Kamatchinathan, S.; Kundu, D. J.; Prakash, A.;  \nFrericks-Zipper, A.; Eisenacher, M.; Walzer, M.; Wang, S.; Brazma, A.; Vizcaíno, J. A. The PRIDE Database Resources in 2022: A  \nHub  for  Mass  Spectrometry-Based  Proteomics  Evidences.  Nucleic  Acids  Res  2022,  50 (D1),  D543–D552.  \nhttps://doi.org/10.1093/nar/gkab1038.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint \n\nFigures\nFigure 1: A) Overall view of the experiments performed with cerebrospinal fluid (CSF) in this work; B) Number of  \nprotein groups identified in neat and enriched CSF samples in the different mass spectrometry platforms; C)  \nPercentage of gain and loss protein groups identification in each of the enriched samples compared to the neat  \nCSF sample (iST-BCT). Different starting volumes for the enrichment protocol were tested: 50 µL (E50), 150 µL  \n(E150),  and  500  µL  (E500);  D)  Correlation  of  precursor  abundances  (logarithmic  scale)  among  technical  \nreplicates in different enriched CSF volumes and neat CSF (iST-BCT); and E) Coefficient of variation for  \nprecursor abundance among triplicate measurements in different enriched CSF volumes and neat CSF (iST-\nBCT).\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint \n\nTables\nTable 1: Detection of specific biomarkers and relevant proteins associated with neurological diseases in both \nneat and enriched cerebrospinal fluid.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint \n\nSupplementary Information\nMaterials and Methods: Detailed description of the LC-MSMS data acquisition methods.\nSupplementary Table S1: List of peptide precursors and protein groups identified in each condition tested.\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint \n\n10 µL CSF\n 50 µL CSF\n(+ 70 µL buffer)\n150 µL CSF\n(+ 200 µL buffer)\n500 µL CSF\n(+ 700 µL buffer)\nLC-MS DIA\nOrbitrap Eclipse Orbitrap Astral\nAstral 30-SPD\nAstral 60-SPD\nE150\nE50 E500\n E150\nE50 E500\nOrbitrap Eclipse\nE150\nE50 E500\n0\n100\n200\n300\n400\n... vs iST−BCT\n% Gain and Loss of Identified of Protein Groups\ngain\nshared\nloss\nAstral 30-SPD\nAstral 60-SPD\n10 50 150 500 10 50 150 500\nOrbitrap Eclipse\n10 50 150 500\n0\n500\n1000\n1500\n2000\n2500\nCSF Starting Volum (µL)\nNumber of Protein Groups Identified\nKit\nENRICH-iST\niST-BCT\nA\nB C\nD E\nr = 0.88 r = 0.92 r = 0.92 r = 0.94\nDigestion Enrichment on paramagnetic beads + Digestion\n.CC-BY 4.0 International licenseavailable under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprintthis version posted October 8, 2024. ; https://doi.org/10.1101/2024.10.07.616086doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}